For the longest time, artificial intelligence (AI) was nothing more than exciting material for science fiction novels and movies. In 1982, Harrison Ford was hunting down replicants in'Blade Runners'; in 2001, Steven Spielberg wove an intricate tale of a relationship between human and machine in'A. Technologies that emulate or surpass human skills and characteristics are fascinating – not only on the big screen. For decades, scientists have dedicated time and money to the topic of artificial intelligence. The workshop'Dartmouth Summer Research Project on Artificial Intelligence', held in 1956 at Dartmouth College in New Hampshire, USA, is seen as the starting point of AI as academic subject.

Energy investors in search of resilience are rediscovering the power of IoT and machine learning to guide data-driven decisions in the face of an increasingly volatile economic environment. Much has happened since I first wrote about a cloud-based IoT platform from Kaiserwetter Energy Asset Management called ARISTOTELES. ARISTOTELES is at the forefront of informed decision-making platforms supporting all stakeholders involved in renewable energy investments. "We're seeing strong interest from investors who want to stay ahead of ongoing shocks related to the COVID-19 pandemic, as well as all the other variables that impact the energy industry," said Hanno Schoklitsch, CEO and founder at Kaiserwetter Energy Asset Management. "Our customers have been super-astonished when they've seen how they could use applied data intelligence on a daily basis to benchmark the performance of the assets they have invested in, and predict production outcomes of renewable energy facilities throughout the world at any given time."

Energy investors in search of resilience are rediscovering the power of the Internet of Things (IoT) and machine learning to guide data-driven decisions in the face of an increasingly volatile economic environment. Much has happened since I first wrote about the cloud-based IoT platform from Kaiserwetter Energy Asset Management called ARISTOTELES. "We're seeing strong interest from investors who want to stay ahead of ongoing shocks related to the COVID-19 pandemic, as well as all the other variables that impact the energy industry," said Hanno Schoklitsch, chief executive officer (CEO) and founder at Kaiserwetter Energy Asset Management. "Our customers have been super astonished when they've seen how they could use applied data intelligence on a daily basis to benchmark the performance of the assets they have invested in and predict production outcomes of renewable energy facilities throughout the world at any given time." It is built on SAP Cloud Platform and uses a number of SAP solutions, including SAP Internet of Things and SAP Analytics Cloud.

MLOps (from Machine Learning and Operations) refers to the process of managing the production lifecycle of Machine Learning models, including also the concept of collaboration between data scientists, data engineers and IT professionals. The objective is to define recommendations and best practices to automate the process, comply with regulatory requirements as well as provide agility to react to changing business requirements. Even though this procedure is mainly of technical nature, companies where MLOps practices are not implemented efficiently face also a number of business and financial challenges. In this blog post I would like to describe the findings and challenges due to inefficient MLOps we have encountered in several customer engagements and to describe how those challenges can be addressed with SAP Data Intelligence, hoping to provide guidance for others in similar situations. It is a common practice in data science teams to develop on local machines and distribute the developed models via shared drives or even email.

UPDATE Oct, 2019: We just added a new season with 4 new podcasts focused on artificial intelligence, machine learning, data science, and data orchestration. Building a data foundation is essential to driving innovation. This is just as true for mid-market companies as for large enterprise companies. Mid-market and large enterprise companies have different challenges, so we've brought together experts from each size company to discuss key trends that are reshaping the way successful companies use their data: from data management and data foundation to spatial and machine learning to data-based process and information excellence. Listen to this chat series on all things data!

UPDATE Oct, 2019: We just added a new season with 4 new podcasts focused on artificial intelligence, machine learning, data science, and data orchestration. Building a data foundation is essential to driving innovation. This is just as true for mid-market companies as for large enterprise companies. Mid-market and large enterprise companies have different challenges, so we've brought together experts from each size company to discuss key trends that are reshaping the way successful companies use their data: from data management and data foundation to spatial and machine learning to data-based process and information excellence. Listen to this chat series on all things data!

SAP formally revealed the planned general availability of hotly anticipated new cloud data storage products during CTO Juergen Mueller's keynote address at TechEd conference in Barcelona Monday. Two of those new offerings -- SAP HANA Cloud and SAP Data Warehouse Cloud, which are bundled under the SAP HANA Cloud Services umbrella -- were announced earlier this year and are expected to ship "before Christmas," Mueller said. SAP HANA Cloud is a new cloud-native version of the core HANA database that is delivered as a container and can be managed on the SAP Cloud platform using the Kubernetes resource manager. According to Mueller, SAP HANA Cloud gives customers a single place to store data from a variety of sources -- for analytics and transaction processing -- and manage it in a variety of ways. "SAP HANA Cloud offers one data access layer for all your data sources," he said.

There's been plenty of hype over artificial intelligence, no question. But there are highly practical ways that CFOs can use AI right now to bring new efficiencies to the enterprise. Here are five of them. The CFO sits at the center of customer data flows: sales data, pricing information, receivables updates – the list goes on. This puts the CFO in a powerful position to link predictive analytics with customer behavior.